Qualitative and quantitative analysis method for autonomously recognizing halogenated organic matters in non-targeted manner

Abstract

The invention discloses a qualitative and quantitative analysis method for autonomously identifying halogenated organic matters in a non-targeted manner, and belongs to the technical field of environmental analytical chemistry and water quality monitoring. The qualitative and quantitative analysis method of the halogenated organic matter comprises the following steps: adding an oxidizing agent or a disinfectant into an original water sample for complete oxidation or disinfection to obtain a water sample to be detected; detecting the water sample to be detected by using ultra-high performance liquid chromatography (UPLC)-Orbitrap mass spectrometry (MS), and detecting the original water sample which is not oxidized or disinfected at the same time to obtain two groups of liquid mass data; performing automatic data processing and contrastive analysis on the two groups of liquid quality data by using Compressed Discovery software, and identifying to obtain a newly generated halogenated organic matter; and establishing a calibration curve by using a total ion chromatogram and an external standard method, and carrying out quantitative analysis on the identified halogenated organic matter. According to the method, the purposes of efficient screening and accurate quantification of the non-targeted halogenated organic matter in the complex water body matrix are achieved, accurate quantification is conducted in combination with an external standard method, and rapid data analysis and result output are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental analytical chemistry and water quality monitoring, and in particular to a qualitative and quantitative analysis method for autonomous non-targeted identification of halogenated organic matter. Background Art

[0002] Halogenated organic matter (especially chlorinated and brominated disinfection byproducts) is the main harmful compound in the water disinfection process, and its monitoring and analysis has always been an important topic in environmental chemistry. However, the limitations of existing analytical techniques include:

[0003] 1) Reliance on targeted detection: Existing methods mostly use gas chromatography-mass spectrometry (GC-MS) or liquid chromatography-mass spectrometry (LC-MS) to quantify known target compounds, but cannot effectively detect unknown halogenated organic compounds.

[0004] 2) Lack of non-targeted analysis capabilities: Faced with complex water sample matrices and unknown halogenated byproducts, existing technologies are unable to efficiently screen and identify compounds that are not included in the target list.

[0005] 3) Manual processing is time-consuming and labor-intensive: Traditional data analysis methods often rely on manual screening, which is inefficient and prone to missing important compounds.

[0006] 4) Insufficient isotope labeling analysis: Chlorine ( 35 Cl / 37 Cl) and bromine ( 79 Br / 81 Br) isotope information has not been fully utilized, which limits the accurate identification of halogenated organic compounds.

[0007] Based on the above problems, there is an urgent need for an efficient, autonomous, non-targeted qualitative and quantitative analysis method for halogenated organic compounds. Summary of the invention

[0008] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide a qualitative and quantitative analysis method for autonomous non-targeted identification of halogenated organic compounds, aiming to solve the problems that the existing methods rely on targeted detection, lack non-targeted analysis capabilities and rely on manual screening.

[0009] The technical solution of the present invention is as follows:

[0010] A qualitative and quantitative analysis method for autonomous non-targeted identification of halogenated organic compounds, comprising the steps of:

[0011] After adding an oxidant or disinfectant to the original water sample for complete oxidation or disinfection, the residual oxidant or disinfectant is removed to obtain a water sample to be tested;

[0012] The water sample to be tested is tested by ultra-high performance liquid chromatography-Orbitrap mass spectrometry, and the original water sample that has not been oxidized or disinfected is tested by ultra-high performance liquid chromatography-Orbitrap mass spectrometry to obtain two sets of liquid-mass data; wherein ultra-high performance liquid chromatography-Orbitrap mass spectrometry can be abbreviated as UPLC-Orbitrap-MS;

[0013] Using Compound Discoverer software to perform automated data processing and comparative analysis on the two sets of LC / MS data to identify newly generated halogenated organic compounds;

[0014] The calibration curve was established by total ion current and external standard method, and the identified halogenated organic compounds were quantitatively analyzed.

[0015] Optionally, the step of using UPLC-Orbitrap-MS to detect the water sample to be tested to obtain liquid quality data specifically includes:

[0016] Pre-treating the water sample to be tested to obtain an organic extract;

[0017] dissolving the organic extract in a mixed solution consisting of methanol and ultrapure water to obtain an organic extract solution;

[0018] The organic extract solution is detected by UPLC-Orbitrap-MS to obtain liquid-to-mass spectrometry data.

[0019] Optionally, the step of pre-treating the water sample to be tested to obtain an organic extract specifically includes: first, using a solid phase extraction column, activating the solid phase extraction column with methanol and ultrapure water in sequence by gravity osmosis, and then balancing the column with ultrapure water; then, acidifying the water sample to be tested with sulfuric acid, and passing it through the solid phase extraction column at a predetermined flow rate, and controlling the flow rate with a vacuum pump; after the water sample to be tested completely passes through the solid phase extraction column, continuing to vacuum to completely remove residual water in the column; then, eluting the target object in the solid phase extraction column with methanol, acetone and dichloromethane in sequence, and collecting all the eluent; finally, evaporating the collected eluent under a nitrogen flow until it is completely dry to obtain an organic extract.

[0020] Optionally, the UPLC parameters for detecting the water sample to be tested by using UPLC-Orbitrap-MS include:

[0021] UPLC separation was performed on a Hypersil Gold C18 column;

[0022] The following gradient elution conditions were used with ultrapure water and methanol as the mobile phase:

[0023] The initial conditions were set to 5% methanol and 95% ultrapure water for 1 min;

[0024] From 1.0 to 13.0 min, the composition of the mobile phase was linearly changed to 95% methanol and 5% ultrapure water;

[0025] Maintain 95% methanol and 5% ultrapure water until 16.0 minutes;

[0026] The flow rate was always maintained at 0.3 mL / min.

[0027] Optionally, the Orbitrap-MS parameters for detecting the water sample to be tested by using UPLC-Orbitrap-MS include:

[0028] Mass spectrometry detection used a heated electrospray ionization source, the spray voltage in both positive and negative ion modes was set to 3600 V, the temperature of the ion transfer tube and evaporator were set to 300 °C, and the flow rates of the sheath gas and auxiliary gas were set to 30 and 10 arbitrary units, respectively;

[0029] The Orbitrap mass spectrometer was operated at a resolution of 120,000, covering the m / z range of 50–650, using data-dependent acquisition mode with a cycle time of 0.6 s;

[0030] MS / MS fragmentation uses high-energy collision dissociation.

[0031] Optionally, the step of using Compound Discoverer software to perform automated data processing and comparative analysis on the two sets of LC / MS data to identify newly generated halogenated organic compounds comprises:

[0032] 1) screening and extracting ultra-high performance liquid chromatography data and high-resolution mass spectrometry data of organic matter from the two sets of liquid-mass spectrometry data;

[0033] 2) Automatically align the retention times of ultra-high performance liquid chromatography data in two sets of LC / MS data;

[0034] 3) Identify the molecular composition of halogenated organic matter based on the isotopic characteristics of halogens, and extract the halogenated organic matter into groups from all organic matter;

[0035] 4) The molecular structures of halogenated organic compounds were identified by searching the ChemSpider, mzCloud and mzVault databases in Compound Discoverer software.

[0036] Optionally, annotate the identified halogenated organic compounds, with the following parameters:

[0037] Isotopic similarity: 90%

[0038] Mass tolerance: 5ppm

[0039] Marking element: halogen

[0040] Strength tolerance: 30%

[0041] Intensity threshold: 0.1%

[0042] Signal-to-noise ratio threshold: 3

[0043] Maximum number of exchanges: 25

[0044] Source efficiency: 100%.

[0045] Optionally, the original water sample originates from drinking water, sewage or natural water body.

[0046] Optionally, the halogenated organic matter is chlorinated disinfection by-products and / or brominated disinfection by-products.

[0047] Optionally, the oxidant is sodium hypochlorite (NaOCl) and the disinfectant is chloramine (NH2Cl).

[0048] Beneficial effects: The present invention aims to provide a qualitative and quantitative analysis method for autonomous non-targeted identification of halogenated organic compounds. By combining UPLC-Orbitrap-MS technology with Compound Discoverer automated data analysis software, the purposes of efficient screening, precise identification and quantification of halogenated organic compounds in complex water matrices are achieved, specifically including: 1) achieving high-sensitivity qualitative analysis of chlorinated and brominated halogenated organic compounds; 2) utilizing halogen isotope characteristics to improve the recognition ability of unknown compounds; 3) combining with external standard method for precise quantification, providing efficient data analysis and result output; 4) providing a universal method that can be applied to different types of water samples. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 Schematic diagram of the Compound Discoverer software analysis process (including detailed steps of data screening, isotope labeling identification, database searching and chemical composition prediction). DETAILED DESCRIPTION

[0050] The present invention provides a qualitative and quantitative analysis method for autonomous non-targeted identification of halogenated organic compounds. In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail below. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0051] The embodiment of the present invention provides a qualitative and quantitative analysis method for autonomous non-targeted identification of halogenated organic compounds, which includes the steps of:

[0052] S1. After adding an oxidant (such as sodium hypochlorite) or a disinfectant (such as chloramine) to the original water sample for complete oxidation or disinfection, the residual oxidant or disinfectant is removed to obtain a water sample to be tested;

[0053] S2, using UPLC-Orbitrap-MS to detect the water sample to be tested, and at the same time using UPLC-Orbitrap-MS to detect the original water sample that has not been oxidized or disinfected, to obtain two sets of liquid quality data;

[0054] S3, using Compound Discoverer software to perform automated data processing and comparative analysis on the two sets of HPLC data to identify newly generated halogenated organic compounds;

[0055] S4. Use the total ion current graph and external standard method to establish a calibration curve and conduct quantitative analysis on the newly generated halogenated organic compounds.

[0056] The embodiment of the present invention is based on UPLC-Orbitrap-MS technology, uses high performance liquid chromatography to separate halogenated organic compounds, and combines high-resolution mass spectrometry detection to achieve accurate measurement within a wide dynamic range. Compound Discoverer software is used for automated data processing to achieve non-targeted qualitative analysis. Combined with the total ion current (TIC) and external standard method, a standard curve is established to perform quantitative analysis of the target; at the same time, background interference is deducted to achieve accurate concentration calculation. The entire analysis process of the embodiment of the present invention is automatically completed by the software, which greatly improves data processing efficiency and result reliability.

[0057] The embodiments of the present invention have the following technical advantages:

[0058] 1) Autonomy and automation: The analysis process does not require much human intervention, and data processing is fully automated, greatly reducing human errors and labor intensity.

[0059] 2) Strong non-targeted analysis capabilities: Using Compound Discoverer software, screening and identification of unknown halogenated organic compounds in complex matrices can be achieved, which significantly improves selectivity and overcomes the limitations of traditional targeted detection.

[0060] 3) High sensitivity and high resolution: Orbitrap-MS technology provides a resolution of 120,000 and detection sensitivity as low as ng / L level, suitable for trace analysis.

[0061] 4) Precision isotope analysis: using halogen (chlorine and bromine) isotope ratios (such as 35 Cl / 37 Cl and 79 Br / 81Br), significantly improving the grouping and identification capabilities of unknown halogenated organic compounds and overcoming the limitations of traditional mass spectrometry (MS) and Fourier transform ion cyclotron resonance mass spectrometry (FT ICR-MS) in isotope utilization.

[0062] 5) Wide applicability: The method is suitable for the detection of halogenated organic matter in drinking water, sewage and natural water bodies, and has good versatility.

[0063] 6) Data query and result verification: The integration of multiple databases (mzCloud, mzVault, ChemSpider, etc.) has greatly improved the efficiency of comprehensive identification and verification of unknown halogenated organic compounds.

[0064] In one embodiment, the step of using Compound Discoverer software to perform automated data processing and comparative analysis on the two sets of liquid chromatography-mass spectrometry data to identify newly generated halogenated organic compounds comprises:

[0065] 1) Screening spectral data: Screening ultra-high performance liquid chromatography data and high-resolution mass spectrometry data of organic matter from the two sets of liquid-mass data;

[0066] 2) Align retention time: Automatically align the retention time of the ultra-high performance liquid chromatography data in the two sets of LC / MS data to correct chromatographic drift and ensure data consistency;

[0067] 3) Detect and group compounds: Identify the molecular composition of halogenated organic matter based on the isotopic characteristics of halogens, and extract the halogenated organic matter into groups from all organic matter;

[0068] 4) Use the database search built into Compound Discoverer software such as ChemSpider, mzCloud and mzVault to identify the molecular structure of halogenated organic compounds.

[0069] In a specific embodiment, the use of Compound Discoverer software (specifically Compound Discoverer 3.3 software) to perform automated data processing and comparative analysis on the mass spectrometry data to identify newly generated halogenated organic compounds includes the following detailed steps:

[0070] 1) Screening spectral data: Screening ultra-high performance liquid chromatography data and high-resolution mass spectrometry data of organic matter from the two sets of liquid-mass data;

[0071] Screening based on a specific m / z range: using chlorine ( 35 Cl / 37 Cl) and bromine ( 79 Br / 81Br) isotope peak ratio and distribution characteristics (i.e. natural abundance ratio) to automatically screen relevant signals in the mass spectrum.

[0072] Peak detection algorithm: Use the maximum signal-to-noise ratio, requiring the signal-to-noise ratio to be greater than 3 to identify the true compound peak and exclude background noise.

[0073] Multidimensional feature matching: Combine parameters such as chromatographic retention time, accurate mass number and isotope abundance distribution to screen out potential target mass spectrometry signals.

[0074] 2) Align retention time: adjust or correct the retention time of each component to correct for chromatographic drift and ensure data consistency;

[0075] Specifically, the chromatographic retention times were aligned through a chromatographic alignment algorithm, chromatographic drift was corrected, and data consistency was ensured.

[0076] The chromatographic alignment algorithm used is dynamic time warping. Dynamic time warping is a chromatographic correction algorithm that aligns the peak positions between samples by nonlinearly adjusting the retention time of the chromatogram. It can correct the drift caused by changes in sample conditions (such as flow rate, column efficiency, etc.).

[0077] Gaussian fitting: Use Gaussian distribution to fit the chromatographic peaks and accurately estimate the retention time of each peak through a mathematical model.

[0078] Peak matching algorithm: Identify common characteristic peaks between samples and use these characteristic peaks as anchor points to establish a calibration model for retention time alignment.

[0079] Principal component analysis: Principal component analysis is used to extract the principal components related to retention time in the samples and perform multidimensional data alignment.

[0080] Linear or Polynomial Regression: Based on the linear or nonlinear change of the chromatographic drift, a regression curve is fitted to correct the drift.

[0081] 3) Detect and group compounds: Identify the molecular composition of halogenated organic matter based on the isotopic characteristics of halogens, and extract the halogenated organic matter into groups from all organic matter;

[0082] 4) Marking of background compounds: distinguishing organic compounds identified in raw water samples that have not been oxidized or disinfected so that they can be distinguished from newly generated halogenated byproducts (i.e., target halogenated organic compounds) in subsequent analysis;

[0083] 5) Predict chemical composition: Predict the chemical composition of possible halogenated organic compounds based on the identified mass spectrometry information;

[0084] 6) Search the mzCloud and mzVault databases: Use the mzCloud and mzVault databases to search and identify the molecular structures of halogenated organic compounds;

[0085] 7) Search ChemSpider: Use the ChemSpider platform to find, verify or obtain more information about the predicted molecular structures of halogenated organic compounds;

[0086] 8) Compound annotation: Annotate the verified halogenated organic compounds. Specific parameters include:

[0087] Isotopic similarity: 90%

[0088] Mass tolerance: 5ppm

[0089] Marking element: halogen

[0090] Strength tolerance: 30%

[0091] Intensity threshold: 0.1%

[0092] Signal-to-noise ratio threshold: 3

[0093] Maximum number of exchanges: 25

[0094] Source efficiency: 100%.

[0095] The above working steps ensure the accurate identification and systematic labeling of halogenated organic compounds.

[0096] Limitations of existing technologies include:

[0097] Low data screening efficiency: Traditional workflows usually rely on manual screening of spectral data, which is time-consuming and labor-intensive and prone to missing important signals;

[0098] Analysis of the impact of chromatographic drift: Failure to effectively correct the chromatographic drift of retention time results in poor data consistency;

[0099] Failure to utilize isotope information: Traditional mass spectrometry techniques (such as MS) and high-resolution mass spectrometry (FT ICR) cannot fully utilize the halogen isotope characteristics and ignore the halogen isotope characteristics (such as 35 Cl / 37 Cl and 79 Br / 81 Br), resulting in insufficient recognition of unknown halogenated organic compounds;

[0100] Reliance on a single database: Existing technologies usually rely on only a single database (such as mzCloud or ChemSpider) for compound matching and identification;

[0101] Limited matching range: Due to the limited coverage of the database, the matching rate of unknown compounds is low, especially in complex matrices, it is difficult to identify new or uncommon compounds;

[0102] Inefficiency: Existing technologies usually require manual intervention during database query, such as screening matching results and manual verification, which is time-consuming and laborious.

[0103] Lack of cross-validation: The results of a single database lack multiple validation, resulting in insufficient reliability and accuracy of the identification results;

[0104] Fragmented processes: lack of fully automated processes from screening to result output;

[0105] The annotation process is not systematic: the compound annotation steps in the existing technology are scattered and lack systematicity, which is prone to duplication of work or information omission;

[0106] Single analysis step: Existing technologies usually handle screening, annotation, and verification as independent steps, making it difficult to achieve efficient comprehensive analysis.

[0107] Compared with the prior art, the embodiments of the present invention have the following technical advantages:

[0108] Precision screening technology: Compound Discoverer software is used to automatically screen characteristic ions of halogenated organic compounds from complex matrices, significantly improving selectivity and coverage;

[0109] Chromatographic correction and data alignment: Use efficient algorithms to screen mass spectrometry data related to target halogenated organic compounds, and use the chromatographic alignment function to align chromatographic retention times, correct chromatographic drift, ensure data consistency, and provide a reliable basis for subsequent analysis;

[0110] Precision isotope analysis: using the isotopic characteristics of halogens (chlorine and bromine) (i.e. 35 Cl / 37 Cl and 79 Br / 81 Br), significantly improving the grouping and identification capabilities of unknown halogenated organic compounds and overcoming the limitations of traditional MS and FT ICR in isotope utilization;

[0111] Multi-database integration: The embodiment of the present invention integrates multiple databases such as mzCloud, mzVault and ChemSpider, providing comprehensive compound search and annotation capabilities, and enhancing the comprehensive identification and verification efficiency of unknown compounds;

[0112] Automated cross-validation: The software automatically integrates and verifies the matching results of each database, avoiding manual screening and improving efficiency and accuracy;

[0113] Improved efficiency and accuracy: Full process automation reduces manual intervention, shortens data processing time, and improves result reliability;

[0114] Real-time update and optimization: Utilize the real-time update capability of online databases to ensure that matching results are based on the latest chemical information;

[0115] Automated analysis process: Figure 1 Demonstrated a complete automated analysis process from data screening to annotation (e.g., selecting spectral data, aligning retention times, detecting compounds, grouping compounds, assigning compound annotations, and database searching), effectively integrating key steps;

[0116] Systematic annotation strategy: Systematic annotation of target compounds is performed through automated tools, including standardized settings of parameters such as isotope similarity and mass tolerance.

[0117] The embodiment of the present invention combines the total ion current (TIC) with the external standard method to establish a standard curve for quantitative analysis of the target; at the same time, background interference is deducted to achieve accurate concentration calculation.

[0118] Limitations of existing technologies include:

[0119] The background interference has a significant impact: Traditional methods are difficult to effectively deduct the background signal in complex matrices, resulting in large deviations in quantitative results;

[0120] Insufficient accuracy of standard curve: The existing external standard method is greatly affected by the matrix effect, and it is difficult to ensure the accuracy and repeatability of the quantitative results;

[0121] Low data processing efficiency: Traditional methods often rely on manual calculations in quantitative analysis, which is inefficient and prone to human errors.

[0122] Compared with the prior art, the technical advantages of the quantitative analysis of the embodiment of the present invention include:

[0123] Combining TIC with external standard method: TIC can detect the concentration of target substances at trace level (ng / L) to meet the needs of high-sensitivity analysis; the peak area of ​​the target substance can be extracted through the total ion current (TIC) and combined with the standard curve to achieve high-sensitivity and high-precision quantitative analysis;

[0124] Background interference subtraction: Add a background subtraction step during the analysis process to effectively eliminate the interference caused by complex matrices and improve the reliability of quantitative results;

[0125] Standard curve optimization: using external standard method to construct high linear correlation (R 2 >0.99) standard curve, and optimize the linear relationship between peak area and concentration through experiments to ensure the accuracy of quantitative analysis;

[0126] Automated processing flow: The entire process from data extraction to result calculation is completed automatically through software, eliminating manual intervention and improving efficiency;

[0127] Adaptable to complex matrices: The method is suitable for complex water samples (such as sewage and natural water bodies) and can still obtain accurate quantitative results under conditions of high matrix interference.

[0128] The present invention is further described in detail below through specific examples.

[0129] Example: Qualitative and quantitative analysis of chlorinated disinfection byproducts (Cl-DBPs) and brominated disinfection byproducts (Br-DBPs) in chloramine-ozone combined treatment based on UPLC-Orbitrap-MS

[0130] In this example, UPLC-Orbitrap-MS combined with Compound Discoverer software was used to achieve qualitative and quantitative analysis of chlorinated disinfection by-products (Cl-DBPs) and brominated disinfection by-products (Br-DBPs) in the chloramine-ozone combined treatment of secondary effluent from municipal sewage, and some of the identified by-products were verified using standard samples, while completing the quantitative analysis experiment.

[0131] 1. Samples and processing methods

[0132] Sample source: Actual municipal sewage secondary effluent samples and model precursor compounds (tannic acid, tryptophan and tyrosine) were used as analysis objects.

[0133] Sample treatment: Actual water samples and model precursor compounds were subjected to ozone treatment alone, monochloramine pretreatment, and monochloramine pretreatment combined with subsequent ozone treatment (monochloramine-ozone combined treatment).

[0134] Through single ozone treatment, monochloramine pretreatment and monochloramine-ozone combined treatment, efficient removal of pollutants and microbial control can be achieved for different water treatment scenarios. Single ozone treatment uses its strong oxidizing ability to quickly degrade organic pollutants and kill microorganisms; monochloramine pretreatment effectively inhibits microbial growth while reducing the formation of by-products; and combined treatment integrates the advantages of both, synergistically removes complex pollutants, improves treatment efficiency and reduces the formation of by-products. The comprehensive use of the above three treatment methods, combined with different water treatment needs, not only optimizes the water purification effect, but also significantly improves the universal applicability of the method, meets diverse water treatment requirements, and ensures the efficiency of the treatment process and the safety of water quality.

[0135] 2. Analysis process

[0136] 1) First, pre-treat the sample to be tested to obtain an organic extract; then detect the organic extract through UPLC-Orbitrap-MS to obtain liquid-mass mass data. The specific steps are as follows:

[0137] The steps for pretreatment of the sample to be tested are as follows: First, a Poly-Sery HLB Pro solid phase extraction column (500 mg, 6 mL) was selected and activated with 10 mL of methanol and 10 mL of ultrapure water in turn by gravity osmosis, and then the column was balanced with ultrapure water to keep the adsorbent wet for standby use. Next, 500 mL of the sample to be tested was acidified to a pH value of 2.0 ± 0.02 with 1 mol / L sulfuric acid (H2SO4), and passed through the solid phase extraction column at a flow rate of 5 mL / min, and the flow rate was controlled by a vacuum pump. After the sample completely passed through the solid phase extraction column, vacuum was continued to be drawn to completely remove the residual water in the column. Then, the target in the solid phase extraction column was eluted with 5 mL of methanol, 2 mL of acetone and 2 mL of dichloromethane in turn, and all the eluents were collected. Finally, the collected eluent was evaporated to complete dryness under a nitrogen flow to obtain an organic extract, which was stored at -20 ° C for standby use. This method can effectively extract target disinfection by-products from water samples and provide sample support for subsequent toxicity testing and high-resolution mass spectrometry analysis.

[0138] The organic extract was dissolved in a mixture of methanol and ultrapure water (1 mL) in a volume ratio of 1:1 to obtain an organic extract solution. An ultra-high performance liquid chromatography (UPLC)-Orbitrap mass spectrometry (MS) system (Thermo Fisher Scientific, USA) was used for analysis. For UPLC separation, 10 μL of the organic extract solution was injected and a gradient of ultrapure water and methanol was used for elution. Mass spectrometry detection was performed using a heated electrospray ionization (ESI) source in positive and negative ion modes, covering the m / z range of 50-650. Detailed UPLC and Orbitrap mass spectrometry parameters are shown below. Each sample was measured three times, and only the molecular formulas identified in at least two measurements were retained. In order to eliminate background interference, blank samples were also included in the experiment for control.

[0139] Detailed UPLC and Orbitrap-MS parameters:

[0140] UPLC separation on Hypersil Gold C 18 The HPLC-MS / MS analysis was performed on a 2.1×100 mm column. For UPLC separation, 10 μL of the organic extract solution was injected into the system. The following gradient elution conditions were used, with ultrapure water (solvent A) and methanol (solvent B) as the mobile phase:

[0141] The initial conditions were set to 5% methanol (B) and 95% ultrapure water (A) for 1 min;

[0142] From 1.0 min to 13.0 min, the composition of the mobile phase was linearly changed to 95% methanol (B) and 5% ultrapure water (A);

[0143] This condition was maintained until 16.0 minutes to re-equilibrate the column. The flow rate was kept constant at 0.3 mL / min throughout the experiment.

[0144] Orbitrap-MS detection used a heated electrospray ionization (ESI) source, and the spray voltage was set to 3600 V in both positive and negative ion modes. The temperature of the ion transfer tube and the evaporator were set to 300°C. The flow rates of the sheath gas and auxiliary gas were set to 30 and 10 arbitrary units, respectively. The Orbitrap mass spectrometer was operated at a resolution of 120,000, covering the m / z range of 50-650. Data-dependent acquisition mode was used with a cycle time of 0.6 seconds. High-energy collision dissociation was used for MS / MS fragmentation, and the normalized collision energies were set to 15, 30, and 45.

[0145] 2) Compound Discoverer 3.3 software (Thermo Fisher Scientific, USA) was used to perform automated data processing and comparative analysis on the liquid quality data obtained from the test to achieve accurate identification of chlorinated and brominated disinfection by-products.

[0146] The detailed parameters for identification of chlorinated and brominated disinfection by-products using Compound Discoverer 3.3 software are as follows:

[0147] CompoundDiscoverer software was used with specific parameters to accurately identify chlorinated disinfection byproducts (Cl-DBPs) and brominated disinfection byproducts (Br-DBPs). A mass tolerance of 5 ppm and a retention time tolerance of 0.1 min were used for feature merging and grouping. A mass tolerance of 5 ppm was used for molecular formula prediction. The molecular formula was restricted to C 1-15 H 1-3 4O 0-12 N 0- 5Cl 0-3 Br 0-2 , H / C≤2.25, O / C≤1.15. The molecular formulas of chlorine and bromine were determined by using the “assign compound annotation” function in Compound Discoverer 3.3 software, combined with 35 Cl / 37 Cl and 79 Br / 81The Br isotope ratio was verified. Impurities generated during solvent and sample preparation were identified and removed by analyzing ultrapure water and methanol (1:1, volume ratio) and procedural blank samples. This method ensures accurate identification of Cl-DBPs and Br-DBPs.

[0148] Figure 1 A detailed workflow for the identification of chlorinated and brominated compounds using Compound Discoverer 3.3 software is presented. First, spectral data related to halogenated disinfection byproducts and their isotopic labels are screened from a large number of data sets. Then, the retention times of the components are aligned by adjusting or correcting the retention times. In the detection and grouping steps, the compounds in the reaction mixture, especially the isotopically labeled halogenated disinfection byproducts, including Cl-DBPs and Br-DBPs, are identified and measured. Subsequently, the background compounds are isotopically labeled so that they can be distinguished from the target halogenated disinfection byproducts in subsequent analysis. Next, the mzCloud and mzVault databases are used to search and identify unknown compounds in the mass spectrometry data, and the chemical composition of possible halogenated disinfection byproducts is predicted based on the mass spectrometry information. In addition, ChemSpider is used to search, verify, or obtain additional information on the predicted composition. Finally, a series of annotations were assigned to the verified halogenated disinfection byproducts and their isotopic labels, including isotopic similarity (90%), mass tolerance (5ppm), labeling element (halogen), intensity tolerance (30%), intensity threshold (0.1%), signal-to-noise threshold (3), maximum exchange number (25), and source efficiency (100%). This process ensures the accurate identification and analysis of chlorinated and brominated disinfection byproducts.

[0149] Through the above method, Cl-DBPs and Br-DBPs including 2,4,6-trichlorophenol and 2,4,6-tribromophenol were identified.

[0150] 3) The calibration curve was established using the external standard method, and the target by-products were quantitatively analyzed in combination with the total ion current (TIC).

[0151] 3. Qualitative analysis results

[0152] 1) Non-targeted identification of halogenated disinfection byproducts in actual water samples

[0153] Using UPLC-Orbitrap-MS combined with Compound Discoverer 3.3 software, several chlorinated and brominated disinfection by-products were identified, including halogenated organic compounds such as 2,4,6-trichlorophenol and 2,4,6-tribromophenol, which could be verified with standards.

[0154] Taking actual water samples as an example, using chloramine-ozone combined treatment, compared with ozone oxidation alone, the number of Cl-DBPs molecules generated increased from 174 to 176, and the number of Br-DBPs molecules increased from 50 to 82, and the relative molecular mass distribution range was 200-400Da.

[0155] 2) Distribution of byproducts of model precursor compounds

[0156] After tannic acid was treated with chloramine-ozone, the number of Cl-DBPs and Br-DBPs molecules generated was significantly higher than that of single treatment, with a wide molecular weight distribution (100-400Da), showing a variety of addition, oxidation and substitution reaction characteristics. The treatment trend of tryptophan and tyrosine was consistent with that of tannic acid, further confirming the universality of the method.

[0157] 4. Quantitative analysis results

[0158] 1) Calibration curve of standard

[0159] Using 2,4,6-trichlorophenol and 2,4,6-tribromophenol standards, concentration gradients (0.1 μg / L to 500 μg / L) were prepared, and the total ion current (TIC) peak area was measured to establish a standard curve and the correlation coefficient (R 2 ) are all over 0.99.

[0160] The standard curve equation is as follows:

[0161] 2,4,6-Trichlorophenol: Peak area = 452.6 × concentration (μg / L) + 120.3

[0162] 2,4,6-Tribromophenol: Peak area = 378.1 × concentration (μg / L) + 95.7

[0163] 2) Determination of target concentration in samples

[0164] 2,4,6-trichlorophenol and 2,4,6-tribromophenol could not be detected in the actual water samples before the chloramine-ozone combined treatment. The concentration of 2,4,6-trichlorophenol in the actual water samples after the chloramine-ozone combined treatment was 22.5μg / L, and the concentration of 2,4,6-tribromophenol was 18.7μg / L, indicating that the monochloramine-ozone combined treatment significantly promoted the formation of such by-products.

[0165] 3) Background subtraction and result correction

[0166] The background interference was subtracted by analyzing the process blank (ultrapure water and methanol, 1:1, volume ratio) to ensure the accuracy of the quantitative results.

[0167] 5. Results Analysis

[0168] This example verifies the feasibility of combining UPLC-Orbitrap-MS with the external standard method for quantitative analysis of halogenated disinfection byproducts, and the use of standard substances further improves the accuracy of the results. Combining the identified halogenated disinfection byproduct information and quantitative results, it is shown that monochloramine-ozone combined treatment significantly increases the concentration of chlorinated and brominated disinfection byproducts, providing an important basis for optimizing water disinfection processes.

[0169] This example demonstrates that the autonomous non-targeted analysis method based on UPLC-Orbitrap-MS and Compound Discoverer software can not only accurately identify chlorinated and brominated disinfection by-products, but also achieve high-precision quantitative analysis of target compounds through the external standard method, which has a wide range of application value.

[0170] In summary, the present invention provides a qualitative and quantitative analysis method for autonomous non-targeted identification of halogenated organic compounds. The present invention aims to solve the problems of low screening efficiency, unsystematic labeling and insufficient chromatographic drift correction in the prior art by attaching Figure 1 The automated workflow shown integrates data screening, chromatographic alignment, compound grouping and database cross-validation, significantly improving analysis efficiency and result reliability. By combining isotope labeling analysis with multi-database search, the accuracy and comprehensiveness of compound identification are significantly improved. The systematic process from spectral feature screening to annotation completion ensures the reliability and repeatability of the results. This workflow has strong adaptability to halogenated organic matter in complex water samples and can cope with high matrix interference conditions. This systematic improvement of the present invention provides an effective solution for the accurate identification and labeling of halogenated organic matter in complex water samples, and realizes the comprehensive identification of halogenated by-products produced during water treatment.

[0171] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A qualitative and quantitative analysis method for autonomous non-targeted identification of halogenated organic compounds, characterized in that: Includes steps: After adding an oxidant or disinfectant to the original water sample for complete oxidation or disinfection, the residual oxidant or disinfectant is removed to obtain a water sample to be tested; The water sample to be tested is tested by ultra-high performance liquid chromatography-Orbitrap mass spectrometry, and the original water sample that has not been oxidized or disinfected is tested by ultra-high performance liquid chromatography-Orbitrap mass spectrometry to obtain two sets of liquid-mass data; Using Compound Discoverer software to perform automated data processing and comparative analysis on the two sets of LC / MS data to identify newly generated halogenated organic compounds; The calibration curve was established by total ion current and external standard method, and the identified unknown halogenated organic compounds were quantitatively analyzed.

2. The method for autonomous non-targeted identification of halogenated organic compounds according to claim 1, characterized in that: The step of using ultra-high performance liquid chromatography-Orbitrap mass spectrometry to detect the water sample to be tested and obtain liquid quality data specifically includes: Pre-treating the water sample to be tested to obtain an organic extract; dissolving the organic extract in a mixed solution consisting of methanol and ultrapure water to obtain an organic extract solution; The organic extract solution is detected by ultra-high performance liquid chromatography-Orbitrap mass spectrometry to obtain liquid-mass spectrometry data.

3. The method for qualitative and quantitative analysis of autonomous non-targeted identification of halogenated organic compounds according to claim 2, characterized in that: The step of pre-treating the water sample to be tested to obtain an organic extract specifically includes: first, using a solid phase extraction column, activating the solid phase extraction column with methanol and ultrapure water in sequence by gravity osmosis, and then balancing the column with ultrapure water; then, acidifying the water sample to be tested with sulfuric acid, and passing it through the solid phase extraction column at a predetermined flow rate, and controlling the flow rate with a vacuum pump; after the water sample to be tested completely passes through the solid phase extraction column, continuing to vacuumize to completely remove residual water in the column; then, eluting the target object in the solid phase extraction column with methanol, acetone and dichloromethane in sequence, and collecting all the eluents; finally, evaporating the collected eluents under a nitrogen flow until they are completely dry to obtain an organic extract.

4. The method for autonomous non-targeted identification of halogenated organic compounds according to claim 1, characterized in that: The ultra-high performance liquid chromatography parameters for detecting the water sample to be tested by ultra-high performance liquid chromatography-Orbitrap mass spectrometry include: Ultra-high performance liquid chromatography separation on Hypersil Gold C 18 On the column; The following gradient elution conditions were used with ultrapure water and methanol as the mobile phase: The initial conditions were set to 5% methanol and 95% ultrapure water for 1 min; From 1.0 to 13.0 min, the composition of the mobile phase was linearly changed to 95% methanol and 5% ultrapure water; Maintain 95% methanol and 5% ultrapure water until 16.0 minutes; The flow rate was always maintained at 0.3 mL / min.

5. The method for qualitative and quantitative analysis of autonomous non-targeted identification of halogenated organic compounds according to claim 1, characterized in that: The Orbitrap mass spectrometry parameters for detecting the water sample to be tested by ultra-high performance liquid chromatography-Orbitrap mass spectrometry include: Mass spectrometry detection used a heated electrospray ionization source, the spray voltage in both positive and negative ion modes was set to 3600 V, the temperature of the ion transfer tube and evaporator were set to 300 °C, and the flow rates of the sheath gas and auxiliary gas were set to 30 and 10 arbitrary units, respectively; The Orbitrap mass spectrometer was operated at a resolution of 120,000, covering the m / z range of 50–650, using data-dependent acquisition mode with a cycle time of 0.6 s; MS / MS fragmentation uses high-energy collision dissociation.

6. The method for autonomous non-targeted identification of halogenated organic compounds according to claim 1, characterized in that: The step of using Compound Discoverer software to automatically process and compare the two sets of liquid chromatography-mass spectrometry data to identify newly generated halogenated organic compounds comprises: 1) screening and extracting ultra-high performance liquid chromatography data and high-resolution mass spectrometry data of organic matter from the two sets of liquid-mass spectrometry data; 2) Automatically align the retention times of ultra-high performance liquid chromatography data in two sets of LC / MS data; 3) Identify the molecular composition of halogenated organic matter based on the isotopic characteristics of halogens, and extract the halogenated organic matter into groups from all organic matter; 4) The molecular structures of halogenated organic compounds were identified by searching the ChemSpider, mzCloud and mzVault databases in Compound Discoverer software.

7. The method for autonomous non-targeted identification of halogenated organic compounds according to claim 6, characterized in that: Annotate the identified halogenated organic compounds. Specific parameters include: Isotopic similarity: 90% Mass tolerance: 5ppm Marking element: halogen Strength tolerance: 30% Intensity threshold: 0.1% Signal-to-noise ratio threshold: 3 Maximum number of exchanges: 25 Source efficiency: 100%.

8. The method for autonomous non-targeted identification of halogenated organic compounds according to claim 1, characterized in that: The original water sample comes from drinking water, sewage or natural water body.

9. The method for qualitative and quantitative analysis of halogenated organic compounds by autonomous non-targeted identification according to claim 1, characterized in that: The halogenated organic matter is chlorinated disinfection by-products and / or brominated disinfection by-products.

10. The method for autonomous non-targeted identification of halogenated organic compounds according to claim 1, characterized in that: The oxidant is sodium hypochlorite, and the disinfectant is chloramine.

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